IDEAS home Printed from https://ideas.repec.org/p/pav/wpaper/160.html
   My bibliography  Save this paper

Monitoring and Improving Greek Banking Services Using Bayesian Networks: an Analysis of Mystery Shopping Data

Author

Listed:
  • Claudia Tarantola

    (Department of Economics, University of Pavia)

  • Paola Vicard

    (Department of Economics, University of Roma Tre)

  • Ioannis Ntzoufras

    (Department of Statistics, Athens University of Economics and Business)

Abstract

Mystery shopping is a well known marketing technique used by companies and marketing analysts to measure quality of service, and gather information about products and services. In this article, we analyse data from mystery shopping surveys via Bayesian networks in order to examine and evaluate the quality of service offered by the loan departments of Greek banks. We use mystery shopping visits to collect information about loan products and services and, by this way, evaluate the customer satisfaction and plan improvement strategies that will assist Banks to reach their internal standards. Bayesian Networks not only provide a pictorial representation of the dependence structure between the characteristics of interest but also allow to evaluate, interpret and understand the effects of possible improvement strategies.

Suggested Citation

  • Claudia Tarantola & Paola Vicard & Ioannis Ntzoufras, 2012. "Monitoring and Improving Greek Banking Services Using Bayesian Networks: an Analysis of Mystery Shopping Data," Quaderni di Dipartimento 160, University of Pavia, Department of Economics and Quantitative Methods.
    • Handle: RePEc:pav:wpaper:160
    as

    Download full text from publisher

    File URL: http://dem-web.unipv.it/web/docs/dipeco/quad/ps/RePEc/pav/wpaper/q160.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. John Mylonakis, 2009. "Customer Relationship Management Functions: A Survey of Greek Bank Customer Satisfaction Perceptions," The IUP Journal of Bank Management, IUP Publications, vol. 0(2), pages 7-31, May.
    2. Lauritzen, Steffen L., 1995. "The EM algorithm for graphical association models with missing data," Computational Statistics & Data Analysis, Elsevier, vol. 19(2), pages 191-201, February.
    3. Mihelis, G. & Grigoroudis, E. & Siskos, Y. & Politis, Y. & Malandrakis, Y., 2001. "Customer satisfaction measurement in the private bank sector," European Journal of Operational Research, Elsevier, vol. 130(2), pages 347-360, April.
    4. Silvia Salini & Ron Kenett, 2009. "Bayesian networks of customer satisfaction survey data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(11), pages 1177-1189.
    5. Ulf Schrader, 2006. "Ignorant advice – customer advisory service for ethical investment funds," Business Strategy and the Environment, Wiley Blackwell, vol. 15(3), pages 200-214, May.
    6. H. Sherman & Joe Zhu, 2006. "Benchmarking with quality-adjusted DEA (Q-DEA) to seek lower-cost high-quality service: Evidence from a U.S.bank application," Annals of Operations Research, Springer, vol. 145(1), pages 301-319, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Galina Timokhina & Lyubov Prokopova & Yuri Gribanov & Stanislav Zaitsev & Natalia Ivashkova & Roman Sidorchuk & Irina Skorobogatykh & Anatoly Shishkin & Zhanna Musatova, 2021. "Digital Customer Experience Mapping in Russian Premium Banking," Economies, MDPI, vol. 9(3), pages 1-24, August.
    2. Di Pietro, Laura & Guglielmetti Mugion, Roberta & Musella, Flaminia & Renzi, Maria Francesca & Vicard, Paola, 2017. "Monitoring an airport check-in process by using Bayesian networks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 106(C), pages 235-247.
    3. F. Cugnata & G. Perucca & S. Salini, 2017. "Bayesian networks and the assessment of universities' value added," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(10), pages 1785-1806, July.
    4. Facchinetti, Silvia & Osmetti, Silvia Angela & Tarantola, Claudia, 2023. "Network models for cyber attacks evaluation," Socio-Economic Planning Sciences, Elsevier, vol. 87(PB).
    5. Eger, Ludvík & MiÄ Ã­k, Michal, 2017. "Customer-oriented communication in retail and Net Promoter Score," Journal of Retailing and Consumer Services, Elsevier, vol. 35(C), pages 142-149.
    6. Flaminia Musella & Paola Vicard, 2015. "Object-oriented Bayesian networks for complex quality management problems," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(1), pages 115-133, January.
    7. E. Cene & F. Karaman, 2015. "Analysing organic food buyers' perceptions with Bayesian networks: a case study in Turkey," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(7), pages 1572-1590, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Anastasiou Athanasios & Kalligosfyris Charalampos & Kalamara Eleni, 2022. "Assessing the effectiveness of tax administration in macroeconomic stability: evidence from 26 European Countries," Economic Change and Restructuring, Springer, vol. 55(4), pages 2237-2261, November.
    2. F. Cugnata & G. Perucca & S. Salini, 2017. "Bayesian networks and the assessment of universities' value added," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(10), pages 1785-1806, July.
    3. Samet Güner & Erman Coşkun, 2016. "Determining the best performing benchmarks for transit routes with a multi-objective model: the implementation and a critique of the two-model approach," Public Transport, Springer, vol. 8(2), pages 205-224, September.
    4. Kuroda, Masahiro & Sakakihara, Michio & Geng, Zhi, 2008. "Acceleration of the EM and ECM algorithms using the Aitken [delta]2 method for log-linear models with partially classified data," Statistics & Probability Letters, Elsevier, vol. 78(15), pages 2332-2338, October.
    5. Moslehpour, Massoud & Lin, Yi Hsin & Nguyen, Thi Le Huyen, 2017. "Top purchase intention priorities of Vietnamese LCC passengers: Expectations and satisfaction," MPRA Paper 81635, University Library of Munich, Germany.
    6. Chen, Yen-Liang & Hu, Hui-Ling, 2006. "An overlapping cluster algorithm to provide non-exhaustive clustering," European Journal of Operational Research, Elsevier, vol. 173(3), pages 762-780, September.
    7. Heena Thanki & Sweety Shah & Harishchandra Singh Rathod & Ankit D. Oza & Dumitru Doru Burduhos-Nergis, 2022. "I Am Ready to Invest in Socially Responsible Investments (SRI) Options Only If the Returns Are Not Compromised: Individual Investors’ Intentions toward SRI," Sustainability, MDPI, vol. 14(18), pages 1-17, September.
    8. Donata Marasini & Piero Quatto & Enrico Ripamonti, 2017. "Inferential confidence intervals for fuzzy analysis of teaching satisfaction," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(4), pages 1513-1529, July.
    9. Federica Cugnata & Silvia Salini, 2014. "Model-based approach for importance–performance analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(6), pages 3053-3064, November.
    10. Feng Li & Qingyuan Zhu & Liang Liang, 2019. "A new data envelopment analysis based approach for fixed cost allocation," Annals of Operations Research, Springer, vol. 274(1), pages 347-372, March.
    11. Croft, J. & Smith, J. Q., 2003. "Discrete mixtures in Bayesian networks with hidden variables: a latent time budget example," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 539-547, January.
    12. Emma Avetisyan & Kai Hockerts, 2017. "Consolidation of the ESG Rating Industry as an Enactment of Institutional Retrogression," Post-Print hal-01695693, HAL.
    13. Obure, Carol Dayo & Jacobs, Rowena & Guinness, Lorna & Mayhew, Susannah & Vassall, Anna, 2016. "Does integration of HIV and sexual and reproductive health services improve technical efficiency in Kenya and Swaziland? An application of a two-stage semi parametric approach incorporating quality me," Social Science & Medicine, Elsevier, vol. 151(C), pages 147-156.
    14. Robert Fabac & Ivan Mance, 2011. "Customer Relationship Management System in Occupational Safety & Health Companies: Research on Practice and Preliminary Design Solution," Interdisciplinary Description of Complex Systems - scientific journal, Croatian Interdisciplinary Society Provider Homepage: http://indecs.eu, vol. 9(2), pages 101-118.
    15. P. Berchialla & S. Snidero & A. Stancu & C. Scarinzi & R. Corradetti & D. Gregori, 2012. "Understanding the epidemiology of foreign body injuries in children using a data-driven Bayesian network," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(4), pages 867-874, September.
    16. Güner, Samet & Seçkin Codal, Keziban, 2022. "Endogenous and exogenous sources of efficiency in the management of Turkish airports," Utilities Policy, Elsevier, vol. 76(C).
    17. Shimshak, Daniel G. & Lenard, Melanie L. & Klimberg, Ronald K., 2009. "Incorporating quality into data envelopment analysis of nursing home performance: A case study," Omega, Elsevier, vol. 37(3), pages 672-685, June.
    18. Lidia Ceriani & Chiara Gigliarano, 2020. "Multidimensional Well-Being: A Bayesian Networks Approach," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 152(1), pages 237-263, November.
    19. Giancarlo MANZI & Pier Alda FERRARI, "undated". "Statistical methods for evaluating satisfaction with public services Abstract: Contrary to private enterprises, public enterprises can be unaware of the impact of their performance when providing serv," CIRIEC Working Papers 1404, CIRIEC - Université de Liège.
    20. Arabatzis, Garyfallos & Grigoroudis, Evangelos, 2010. "Visitors' satisfaction, perceptions and gap analysis: The case of Dadia-Lefkimi-Souflion National Park," Forest Policy and Economics, Elsevier, vol. 12(3), pages 163-172, March.

    More about this item

    Keywords

    Bayesian networks; Customer satisfaction; Mystery shopping; Service quality improvement.;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pav:wpaper:160. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Paolo Bonomolo (email available below). General contact details of provider: https://edirc.repec.org/data/dppavit.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.